Social Anxiety as Predictor of Depression in Adolescents: Mediating Role of Dysfunctional Daydreaming

OBJECTIVE: To investigate the relationship among Dysfunctional Daydreaming, Social Anxiety and Depression in adolescents and explore the mediating role of daydreaming between two variables. METHODOLOGY: Cross-sectional correlational research design was conducted to collect data from 200 participants (Males=94, Females=106) of age range 12 to 19 years (M=17.08, SD= 1.93) with a convenient sampling technique via google form due to COVID-19 during the lockdown period April to June 2020. Inclusion criteria focused on willing participants who were studying online during the pandemic. Unwilling students, along with the freezing of study, were excluded. Three standardized questionnaires, Patient Health Questionnaire (PHQ-9) for measuring Depression, the Social Anxiety Scale for Adolescents (SAS-A) for measuring Social Anxiety and Dysfunctional Daydreaming Scale (DDS) for measuring malfunctioning fantasies, were used. The demographic sheet and informed consent form were filled out correctly to administer the questionnaires. Data were analyzed in SPSS-24 for frequencies, percentages, and correlations. Mediation analysis was conducted in AMOS-24. RESULT(S): Pearson Product Moment Correlation Coefficient revealed a positive correlation of Dysfunctional Daydreaming with Social anxiety (r=.50, p<0.05) and Depression (r=0.58, p<0.05). Dysfunctional daydreaming mediated between social anxiety and Depression in adolescents (beta=0.34, p<0.05). CONCLUSION(S): The research indicated social support as a predictor of Depression in adolescents with a strengthening impact of abnormal Daydreaming on Depression. An implication for mindfulness interventions that focus on the present moment without judgment is necessary to regulate anxiety, Depression, and daydreaming in college students. Copyright © 2022, Liaquat University of Medical and Health Sciences. All rights reserved.

Covid-19 detection from X-ray images using Customized Convolutional Neural Network

COVID-19 continues to have a devastating impact on the lives of people all over the world. Various new technologies arose in the research environment to assist mankind in surviving and living a better life. It is important to screen the infected patients in a timely and cost-effective manner to combat this disease and avoid its transmission. To achieve this aim, detection of Covid-19 from radiological evaluation of chest x-ray images using deep learning algorithms is less expensive and easily available option as it ensures fast and efficient diagnosis of the disease. Therefore, this paper presents a novel customized convolutional neural network (CNN) approach for the detection of COVID-19 from chest x-ray images. The performance of the proposed model is evaluated on three different size datasets, created from publicly available datasets. Experimental results show that the proposed model has better performance on Dataset 2. A very large increase or decrease of the number of samples in the dataset degrades the performance of the proposed model. The performance of the CNN model is compared with traditional pretrained networks namely VGG-16, VGG-19, ResNet-50 and Inception-V3. All the models show promising performance on dataset 2 which shows that optimum amount of data is enough for the model to lean features from the input data. Overall, the best validation accuracy of 97.78 was achieved by the proposed model on dataset 2. © 2022 IEEE.

H3DNN: 3D DEEP LEARNING BASED DETECTION OF COVID-19 VIRUS USING LUNGS COMPUTED TOMOGRAPHY

With the rapid spread of the novel COVID-19 virus, there is an increasing demand for screening COVID-19 patients. Typical methods for screening coronavirus patients have a large false detection rate. An effective and reliable screening method for detecting coronavirus is required. For this reason, some other reliable methods such as Computed Tomography (CT) imaging is employed to detect coronavirus accurately. In this paper, we present a 3D-Deep learning based method that automatically screens coronavirus patients using 3D volumetric CT image data. Our proposed system assists medical practitioners to effectively screen out COVID-19 patients. We performed extensive experiments on two datasets i.e., CC-19 and COVID-CT using various state-of-the-art 3D Deep learning based methods including 3D ResNets, C3D, 3D DenseNets, I3D, and LRCN. The results of the experiments show the competitive effectiveness of our proposed approach.